Triple
T1033974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soho |
E22316
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Mayfair |
E11937
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Mayfair | Statement: [Soho, adjacentTo, Mayfair]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mayfair Context triple: [Soho, adjacentTo, Mayfair]
-
A.
Mayfair
Mayfair is a residential neighborhood located within the city of Homewood, Alabama.
-
B.
Knightsbridge
Knightsbridge is an affluent central London district renowned for its luxury shopping, upscale residences, and proximity to Hyde Park.
-
C.
Mayfair, London, England
chosen
Mayfair, London, England is an affluent central London district known for its luxury residences, exclusive shops, and prestigious hotels and clubs.
-
D.
Grosvenor
Grosvenor is the middle name of Bertram Grosvenor Goodhue, a prominent American architect and designer known for his influential early 20th-century works.
-
E.
Soho
Soho is a vibrant central London district famed for its nightlife, entertainment venues, and diverse cultural scene.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b814c16c8190ac4d20feecdadbae |
completed | March 1, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac3bc15a6c81909a71bf17b5cd4019 |
completed | March 7, 2026, 2:52 p.m. |
Created at: March 1, 2026, 7:41 p.m.